Showing papers by "Linda See published in 1999"
TL;DR: The overall results indicate that this methodology may provide a well performing, low-cost solution, which may be readily integrated into existing operational flood forecasting and warning systems.
Abstract: This paper assesses one of many potential enhancements to conventional flood forecasting that can be achieved through the use of soft computing technologies. A methodology is outlined in which the forecasting data set is split into subsets before training with a series of neural networks. These networks are then recombined via a rule-based fuzzy logic model that has been optimized using a genetic algorithm. The methodology is demonstrated using historical time series data from the Ouse River catchment in northern England. The model forecasts are assessed on global performance statistics and on a more specific flood-related evaluation measure, and they are compared to benchmarks from a statistical model and naive predictions. The overall results indicate that this methodology may provide a well performing, low-cost solution, which may be readily integrated into existing operational flood forecasting and warning systems.
139 citations
TL;DR: In this article, four design tool procedures are examined to create improved neural network architectures for forecasting runoff from a small catchment, and algorithms are used to remove nodes and connections so as to produce an optimised forecasting model, thereby reducing computational expense without loss in performance.
Abstract: Four design tool procedures are examined to create improved neural network architectures for forecasting runoff from a small catchment Different algorithms are used to remove nodes and connections so as to produce an optimised forecasting model, thereby reducing computational expense without loss in performance The results also highlight issues in selecting analytical methods to compare outputs from different forecasting procedures
57 citations
TL;DR: Measurements of water pressure beneath Trapridge Glacier, Yukon Territory, Canada show that the basal water system is highly heterogeneous and the suitability of the computational intelligence techniques to model these data increases with the complexity of the system to be modelled.
Abstract: Measurements of water pressure beneath Trapridge Glacier, Yukon Territory, Canada show that the basal water system is highly heterogeneous. Three types of behaviour were recorded: pressure records which are strongly correlated, records which are strongly anticorrelated, and records which alternate between strong correlation and strong anticorrelation. We take the pressure in bore-holes that are connected to the evacuation route for basal water as the forcing, and the other pressures as the response to this forcing. Previous work (Murray and Clarke 1995) has shown that these relationships can be modelled using low-order nonlinear differential equations optimized by inversion. However, despite optimizing the model parameters we cannot be sure that the final model forms are themselves optimal. Computational intelligence techniques provide alternative methods for fitting models and are robust to missing or noisy data, applicable to non-smooth models, and attempt to derive optimal model forms as well as optimal model parameters. Four computational intelligence techniques have been used and the results compared with the more conventional mathematical model. These methods were genetic programming, artificial neural networks, fuzzy logic and self-organizing maps. We compare each technique and offer an evaluation of their suitability for modelling the pressure data. The evaluation criteria are threefold: (1) goodness of fit and an ability to predict subsequent data under different surface weather conditions; (2) interpretability, and the extent and significance of any new insights offered into the physics of the glacier; (3) computation time. The results suggest that the suitability of the computational intelligence techniques to model these data increases with the complexity of the system to be modelled.
23 citations
10 Jul 1999
TL;DR: Seven fusion strategies were used to perform an alliance of river level forecasts and while gains were obtained on the timing of events for a stable regime, it proved difficult to correct for poor peak flow prediction an a flashier catchment.
Abstract: Seven fusion strategies were used to perform an alliance of river level forecasts. The original data comprised persistence values and continuous predictions produced using a set of conventional, fuzzy logic and neural network models for two contrasting catchments: the River Ouse and the Upper River Wye. The fusion process followed a simple "data-in-data-out" architecture and each fusion operation was implemented using a backpropagation neural network. Worthwhile gains were obtained on the timing of events for a stable regime, but it proved difficult to correct for poor peak flow prediction an a flashier catchment.
3 citations